Deep reinforcement learning for traffic signal control under disturbances: A case study on Sunway city, Malaysia

Rasheed, Faizan; Yau, Kok‐Lim Alvin; Low, Yeh Ching

doi:10.1016/j.future.2020.03.065

Cited by 54 publications

(43 citation statements)

References 13 publications

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“…Other recent studies have also used MARL for TLC [116,117,118,119]. General related computational frameworks for RL control applications have been explored for multi-objective decision modeling in [120] and for a hybrid fuzzy and RL control in [121].…”

Section: Traffic Light Controlmentioning

confidence: 99%

“…Issue References Role Traffic management Traffic light control [112,118,121] Solve the TLC problem for one intersection using full vehicle detection [113] Solve the TLC problem for one intersection using partial vehicle detection [114,115,116,117,119] Solve the TLC problem for multiple intersection Variable speed limit control [122,123,124,125,126] Set the speed limit to keep the bottleneck density below its critical value [127] A comprehensive survey on the state-of-the-art of RL-VSL Vehicle management Motion control and trajectory planning [128,129,130] Surveys for vehicle management using RL/DRL…”

Section: Resourcementioning

confidence: 99%

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Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

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Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performance under uncertainty. To achieve this goal, Reinforcement Learning (RL) can effectively solve decision-making problems. However, the state and action spaces are massive and complex in large-scale wireless networks. Hence, RL may not be able to find the best strategy in a reasonable time. Therefore, Deep Reinforcement Learning (DRL) has been developed to combine RL with Deep Learning (DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief overview of RL and DRL concepts. Then we review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. We finally discuss and highlight some unresolved challenges for further study.

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Section: Traffic Light Controlmentioning

confidence: 99%

Section: Resourcementioning

confidence: 99%

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

View full text Add to dashboard Cite

show abstract

“…Because when it is difficult to develop a model for a controlled system, we can use the system input and output data to implement control and decision-making; In recent years, breakthroughs in artificial intelligence theory and methods and the evolution of largescale cloud computing and edge computing technologies have promoted the development of new types of intelligent control centered on artificial intelligence methods. Some scholars have proposed artificial intelligence-based traffic control theory and method [24][25][26][27], which is characterized by advancement, prevention, and initiative.…”

Section: Traditional Intersection Traffic Controlmentioning

confidence: 99%

Urban Intersection Signal Control Based on Time-Space Resource Scheduling

et al. 2021

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This paper improves the level of urban traffic control by creasing the dimension of control variables. It focuses on roads rather than vehicles. A new space-time resource scheduling model and a bilevel optimization control method for urban intersections are developed in this study. In traditional concept, the properties of lane are fixed. Nowadays, it changes with the development of new technologies, which increase the dimension of the control variables in the control model and expand the control capability. To this end, the space-time resource scheduling model for intersections includes spatial variables (lane genes, phases, and phase sequences) and time variables (green light time of phases). Then, a new bi-level optimization control method is developed, in which there are an upper layer for lane control based on reinforcement learning and a lower layer is a two-layer optimal control method of phase control based on the model predictive control idea. Finally, the proposed method is proved more efficient than traditional methods after comprehensive experiments. INDEX TERMS V2I; time-space resource scheduling model; lane genes; dual-layer optimization; reinforcement learning; model predictive control

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“…The evolution in machine learning have produced a deep reinforcement learning techniques [7,8] which have been applied for traffic signal control in many works [9,10]. Authors [11][12][13] used the extensive reinforcement learning for traffic signal control provides a numerous possible state representations as: vehicle density, flow, queue, location, speed along with the current traffic phase, cycle length and red time.…”

Section: Related Workmentioning

confidence: 99%

Traffic Signal Control Model on Isolated Intersection Using Reinforcement Learning: A Case Study on Algiers City, Algeria

Bouriachi¹,

Zatla²,

Tolbi³

et al. 2021

RIA

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Traffic jams and congestion in our cities are a major problem because of the huge increase in the number of cars on the road. To remedy this problem, several control methods are proposed to prevent or reduce traffic congestion based on traffic lights. There are few works using reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing works have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we proposed a reinforcement learning algorithm to address the traffic signal control problem in real multi-phases isolated intersection. A case study based on Algiers city is conducted the simulation results from the different scenarios show that our proposed scheme reduces the total travel time of the vehicles compared to those obtained with traffic-adaptive control.

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Deep reinforcement learning for traffic signal control under disturbances: A case study on Sunway city, Malaysia

Cited by 54 publications

References 13 publications

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Urban Intersection Signal Control Based on Time-Space Resource Scheduling

Traffic Signal Control Model on Isolated Intersection Using Reinforcement Learning: A Case Study on Algiers City, Algeria

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